Yt Competitive Analysis

---
name: yt-competitive-analysis
description: >-
  Analyze YouTube channels for outlier videos and packaging patterns. Identifies what's working (2x+ average views) across any set of channels. Use when asked for YouTube competitive analysis, viral video patterns, or packaging/title inspiration.
version: 1.0.1
display_name: "yt-competitive-analysis"
display_name_en: "Yt Competitive Analysis"
description_zh: "YouTube 竞品分析，发现爆款视频与标题包装规律"
description_en: "YouTube competitive analysis: outlier video detection and packaging patterns"
visibility: "public"
---

# YouTube Competitive Analysis

Outlier detection and packaging pattern extraction for YouTube channels.

## When to Use

- User asks for YouTube competitive analysis
- User wants to find viral video patterns
- User wants packaging/title inspiration from specific creators
- User wants to track competitor YouTube performance

## Prerequisites

- YouTube Data API v3 key set as `$YOUTUBE_API_KEY`

## Usage

```bash
# Analyze specific channels
python3 analyze.py "$YOUTUBE_API_KEY" --channels "@handle1,@handle2" --days 30

# Use predefined sets
python3 analyze.py "$YOUTUBE_API_KEY" --set ai
python3 analyze.py "$YOUTUBE_API_KEY" --set business
python3 analyze.py "$YOUTUBE_API_KEY" --set both

# Export formats
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output json
python3 analyze.py "$YOUTUBE_API_KEY" --set both --output console
```

## Predefined Channel Sets

**AI Creators:** Jeff Su, Alex Finn, Riley Brown, Dan Martell, Matt Wolfe, Nate Herk, Grace Leung, Matt Berman

**Business Creators:** Alex Hormozi, Gary Vaynerchuk, Patrick Bet-David, Codie Sanchez, Leila Hormozi, Iman Gadzhi, My First Million

## Output Interpretation

- **Multiplier**: Times above channel average (2.0x = double normal)
- **Outlier threshold**: 2x average. Study anything above this.
- **Title patterns**: Common words in outlier titles indicate proven formats
- **Cadence**: Videos per week. Higher cadence creators may have lower per-video averages.

## Channel Analytics Feedback Loop

Competitive analysis is only half the loop. When you have access to the channel's own analytics, compare candidate packaging against actual performance after publish.

Before recommending a package:
- Pull channel baseline by topic, title pattern, thumbnail pattern, length, publish day/time, and format.
- Check impressions, CTR, average view duration, retention curve, watch time, subscribers gained, comments, and traffic source.
- Compare the proposed title/thumbnail/hook against similar historical videos and competitor outliers.

After publishing:
1. Log video ID, title, thumbnail concept, hook, topic bucket, retention devices, and publish date.
2. Pull analytics after the chosen readback window.
3. Compare baseline vs candidate.
4. Patch title formulas, thumbnail rules, hook patterns, retention beats, chapter structure, Shorts selection, or repurposing guidance only if the candidate wins.

Readback windows:
- 24-48 hours for CTR and early retention
- 7 days for average view duration and watch time
- 28 days for topic durability and subscriber gain

Do not call packaging validated until analytics are checked. Otherwise it is a screenplay wearing a lab coat.

## Packaging Skeletons (Proven Formats)

**Long-form:**
- "X, Clearly Explained"
- "X hours of Y in Z minutes"
- "The Laziest Way to X"
- "Give me X minutes and I'll Y"
- "X INSANE Use Cases for Y"

**Shorts:**
- "2024 vs 2025 X" (year comparison)
- "Bad Good Great X" (tier ranking)
- "Stop doing X, do Y instead" (contrarian)